def _set_TUB(self, mesh_size):
geo = dmsh.Polygon([[1 / 3, 1 / 3], [1, 0], [0.5, 0.5]])
points, triangles = dmsh.generate(geo, mesh_size)
self._centroids = np.empty((len(triangles), 2))
for i, triangle in enumerate(triangles):
self._centroids[i] = np.mean(points[triangle], axis=0)
tri = Triangulation(points[:, 0], points[:, 1], triangles)
self._trifinder = tri.get_trifinder()
def computeDeformation(X, Y, Zx, Zy, shape):
''' Computes the error, or the deformation between the matched keypoints of both images,
at each pixel position in the second image.
Input(s):
X: vertical coordinate of the keypoints, including fringe keypoints
Y: horizontal coordinate of the keypoints, including fringe keypoints
Zx: vertical deformation of the keypoints
Zy: horizontal deformation of the keypoints
shape: shape of the image to deform
Output(s):
dx: map of the vertical deformation, at each pixel position in the 2nd image
dy: map of the horizontal deformation, at each pixel position in the 2nd image
'''
triangulation = Triangulation(X, Y)
finder = triangulation.get_trifinder()
triangle = np.zeros(shape)
j_coords = np.arange(shape[1])
for i in range(shape[0]):
triangle[i] = finder(i * np.ones(shape[1]).astype('int64'), j_coords)
array_x = triangulation.calculate_plane_coefficients(Zx)
array_y = triangulation.calculate_plane_coefficients(Zy)
n_triangle = array_x.shape[0]
dx, dy = np.zeros(shape), np.zeros(shape)
indices = np.indices(shape)
dx = indices[0]*array_x[:,0][triangle.astype('int16')] + indices[1]*array_x[:, 1][triangle.astype('int16')] + \
array_x[:,2][triangle.astype('int16')]
dy = indices[0]*array_y[:,0][triangle.astype('int16')] + indices[1]*array_y[:, 1][triangle.astype('int16')] + \
array_y[:,2][triangle.astype('int16')]
return dx, dy
event.canvas.draw()
# Create a Triangulation.
n_angles = 16
n_radii = 5
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
triangulation = Triangulation(x, y)
xmid = x[triangulation.triangles].mean(axis=1)
ymid = y[triangulation.triangles].mean(axis=1)
mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
triangulation.set_mask(mask)
# Use the triangulation's default TriFinder object.
trifinder = triangulation.get_trifinder()
# Setup plot and callbacks.
plt.subplot(111, aspect='equal')
plt.triplot(triangulation, 'bo-')
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
update_polygon(-1)
plt.gca().add_patch(polygon)
plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)
plt.show()
plt.title('In triangle %i' % tri)
event.canvas.draw()
# Create a Triangulation.
n_angles = 16
n_radii = 5
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi / n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
triang = Triangulation(x, y)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)
# Use the triangulation's default TriFinder object.
trifinder = triang.get_trifinder()
# Setup plot and callbacks.
plt.subplot(111, aspect='equal')
plt.triplot(triang, 'bo-')
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
update_polygon(-1)
plt.gca().add_patch(polygon)
plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)
plt.show()
event.canvas.draw()
# Create a Triangulation.
n_angles = 16
n_radii = 5
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2 * math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi / n_angles
x = (radii * np.cos(angles)).flatten()
y = (radii * np.sin(angles)).flatten()
triangulation = Triangulation(x, y)
xmid = x[triangulation.triangles].mean(axis=1)
ymid = y[triangulation.triangles].mean(axis=1)
mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
triangulation.set_mask(mask)
# Use the triangulation's default TriFinder object.
trifinder = triangulation.get_trifinder()
# Setup plot and callbacks.
plt.subplot(111, aspect='equal')
plt.triplot(triangulation, 'bo-')
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for xs,ys
update_polygon(-1)
plt.gca().add_patch(polygon)
plt.gcf().canvas.mpl_connect('motion_notify_event', motion_notify)
plt.show()
class MyFigure(FigureCanvas):
"""__图像显示类__
三个子图的初始化 & 三种重建图像的显示方式
"""
def __init__(self, width, height, dpi):
self.fig = plt.figure(figsize=(width, height), dpi=dpi)
super(MyFigure, self).__init__(self.fig)
self.gs = GridSpec(3, 2) #画布布局
# 用于三角剖分的顶点坐标
self.nodes = np.array([[0.0000e+00, 0.0000e+00],
[0.0000e+00, 8.3333e-02],
[8.3333e-02, 5.1027e-18],
[1.0205e-17, -8.3333e-02],
[-8.3333e-02, -1.5308e-17],
[0.0000e+00, 1.6667e-01],
[1.1785e-01, 1.1785e-01],
[1.6667e-01, 1.0205e-17],
[1.1785e-01, -1.1785e-01],
[2.0411e-17, -1.6667e-01],
[-1.1785e-01, -1.1785e-01],
[-1.6667e-01, -3.0616e-17],
[-1.1785e-01, 1.1785e-01],
[0.0000e+00, 2.5000e-01],
[1.2500e-01, 2.1651e-01],
[2.1651e-01, 1.2500e-01],
[2.5000e-01, 1.5308e-17],
[2.1651e-01, -1.2500e-01],
[1.2500e-01, -2.1651e-01],
[3.0616e-17, -2.5000e-01],
[-1.2500e-01, -2.1651e-01],
[-2.1651e-01, -1.2500e-01],
[-2.5000e-01, -4.5924e-17],
[-2.1651e-01, 1.2500e-01],
[-1.2500e-01, 2.1651e-01],
[0.0000e+00, 3.3333e-01],
[1.2756e-01, 3.0796e-01],
[2.3570e-01, 2.3570e-01],
[3.0796e-01, 1.2756e-01],
[3.3333e-01, 2.0411e-17],
[3.0796e-01, -1.2756e-01],
[2.3570e-01, -2.3570e-01],
[1.2756e-01, -3.0796e-01],
[4.0822e-17, -3.3333e-01],
[-1.2756e-01, -3.0796e-01],
[-2.3570e-01, -2.3570e-01],
[-3.0796e-01, -1.2756e-01],
[-3.3333e-01, -6.1232e-17],
[-3.0796e-01, 1.2756e-01],
[-2.3570e-01, 2.3570e-01],
[-1.2756e-01, 3.0796e-01],
[0.0000e+00, 4.1667e-01],
[1.2876e-01, 3.9627e-01],
[2.4491e-01, 3.3709e-01],
[3.3709e-01, 2.4491e-01],
[3.9627e-01, 1.2876e-01],
[4.1667e-01, 2.5513e-17],
[3.9627e-01, -1.2876e-01],
[3.3709e-01, -2.4491e-01],
[2.4491e-01, -3.3709e-01],
[1.2876e-01, -3.9627e-01],
[5.1027e-17, -4.1667e-01],
[-1.2876e-01, -3.9627e-01],
[-2.4491e-01, -3.3709e-01],
[-3.3709e-01, -2.4491e-01],
[-3.9627e-01, -1.2876e-01],
[-4.1667e-01, -7.6540e-17],
[-3.9627e-01, 1.2876e-01],
[-3.3709e-01, 2.4491e-01],
[-2.4491e-01, 3.3709e-01],
[-1.2876e-01, 3.9627e-01],
[0.0000e+00, 5.0000e-01],
[1.2941e-01, 4.8296e-01],
[2.5000e-01, 4.3301e-01],
[3.5355e-01, 3.5355e-01],
[4.3301e-01, 2.5000e-01],
[4.8296e-01, 1.2941e-01],
[5.0000e-01, 3.0616e-17],
[4.8296e-01, -1.2941e-01],
[4.3301e-01, -2.5000e-01],
[3.5355e-01, -3.5355e-01],
[2.5000e-01, -4.3301e-01],
[1.2941e-01, -4.8296e-01],
[6.1232e-17, -5.0000e-01],
[-1.2941e-01, -4.8296e-01],
[-2.5000e-01, -4.3301e-01],
[-3.5355e-01, -3.5355e-01],
[-4.3301e-01, -2.5000e-01],
[-4.8296e-01, -1.2941e-01],
[-5.0000e-01, -9.1849e-17],
[-4.8296e-01, 1.2941e-01],
[-4.3301e-01, 2.5000e-01],
[-3.5355e-01, 3.5355e-01],
[-2.5000e-01, 4.3301e-01],
[-1.2941e-01, 4.8296e-01],
[0.0000e+00, 5.8333e-01],
[1.2980e-01, 5.6871e-01],
[2.5310e-01, 5.2557e-01],
[3.6370e-01, 4.5607e-01],
[4.5607e-01, 3.6370e-01],
[5.2557e-01, 2.5310e-01],
[5.6871e-01, 1.2980e-01],
[5.8333e-01, 3.5719e-17],
[5.6871e-01, -1.2980e-01],
[5.2557e-01, -2.5310e-01],
[4.5607e-01, -3.6370e-01],
[3.6370e-01, -4.5607e-01],
[2.5310e-01, -5.2557e-01],
[1.2980e-01, -5.6871e-01],
[7.1438e-17, -5.8333e-01],
[-1.2980e-01, -5.6871e-01],
[-2.5310e-01, -5.2557e-01],
[-3.6370e-01, -4.5607e-01],
[-4.5607e-01, -3.6370e-01],
[-5.2557e-01, -2.5310e-01],
[-5.6871e-01, -1.2980e-01],
[-5.8333e-01, -1.0716e-16],
[-5.6871e-01, 1.2980e-01],
[-5.2557e-01, 2.5310e-01],
[-4.5607e-01, 3.6370e-01],
[-3.6370e-01, 4.5607e-01],
[-2.5310e-01, 5.2557e-01],
[-1.2980e-01, 5.6871e-01],
[0.0000e+00, 6.6667e-01],
[1.3006e-01, 6.5386e-01],
[2.5512e-01, 6.1592e-01],
[3.7038e-01, 5.5431e-01],
[4.7140e-01, 4.7140e-01],
[5.5431e-01, 3.7038e-01],
[6.1592e-01, 2.5512e-01],
[6.5386e-01, 1.3006e-01],
[6.6667e-01, 4.0822e-17],
[6.5386e-01, -1.3006e-01],
[6.1592e-01, -2.5512e-01],
[5.5431e-01, -3.7038e-01],
[4.7140e-01, -4.7140e-01],
[3.7038e-01, -5.5431e-01],
[2.5512e-01, -6.1592e-01],
[1.3006e-01, -6.5386e-01],
[8.1643e-17, -6.6667e-01],
[-1.3006e-01, -6.5386e-01],
[-2.5512e-01, -6.1592e-01],
[-3.7038e-01, -5.5431e-01],
[-4.7140e-01, -4.7140e-01],
[-5.5431e-01, -3.7038e-01],
[-6.1592e-01, -2.5512e-01],
[-6.5386e-01, -1.3006e-01],
[-6.6667e-01, -1.2246e-16],
[-6.5386e-01, 1.3006e-01],
[-6.1592e-01, 2.5512e-01],
[-5.5431e-01, 3.7038e-01],
[-4.7140e-01, 4.7140e-01],
[-3.7038e-01, 5.5431e-01],
[-2.5512e-01, 6.1592e-01],
[-1.3006e-01, 6.5386e-01],
[0.0000e+00, 7.5000e-01],
[1.3024e-01, 7.3861e-01],
[2.5652e-01, 7.0477e-01],
[3.7500e-01, 6.4952e-01],
[4.8209e-01, 5.7453e-01],
[5.7453e-01, 4.8209e-01],
[6.4952e-01, 3.7500e-01],
[7.0477e-01, 2.5652e-01],
[7.3861e-01, 1.3024e-01],
[7.5000e-01, 4.5924e-17],
[7.3861e-01, -1.3024e-01],
[7.0477e-01, -2.5652e-01],
[6.4952e-01, -3.7500e-01],
[5.7453e-01, -4.8209e-01],
[4.8209e-01, -5.7453e-01],
[3.7500e-01, -6.4952e-01],
[2.5652e-01, -7.0477e-01],
[1.3024e-01, -7.3861e-01],
[9.1849e-17, -7.5000e-01],
[-1.3024e-01, -7.3861e-01],
[-2.5652e-01, -7.0477e-01],
[-3.7500e-01, -6.4952e-01],
[-4.8209e-01, -5.7453e-01],
[-5.7453e-01, -4.8209e-01],
[-6.4952e-01, -3.7500e-01],
[-7.0477e-01, -2.5652e-01],
[-7.3861e-01, -1.3024e-01],
[-7.5000e-01, -1.3777e-16],
[-7.3861e-01, 1.3024e-01],
[-7.0477e-01, 2.5652e-01],
[-6.4952e-01, 3.7500e-01],
[-5.7453e-01, 4.8209e-01],
[-4.8209e-01, 5.7453e-01],
[-3.7500e-01, 6.4952e-01],
[-2.5652e-01, 7.0477e-01],
[-1.3024e-01, 7.3861e-01],
[0.0000e+00, 8.3333e-01],
[1.3036e-01, 8.2307e-01],
[2.5751e-01, 7.9255e-01],
[3.7833e-01, 7.4251e-01],
[4.8982e-01, 6.7418e-01],
[5.8926e-01, 5.8926e-01],
[6.7418e-01, 4.8982e-01],
[7.4251e-01, 3.7833e-01],
[7.9255e-01, 2.5751e-01],
[8.2307e-01, 1.3036e-01],
[8.3333e-01, 5.1027e-17],
[8.2307e-01, -1.3036e-01],
[7.9255e-01, -2.5751e-01],
[7.4251e-01, -3.7833e-01],
[6.7418e-01, -4.8982e-01],
[5.8926e-01, -5.8926e-01],
[4.8982e-01, -6.7418e-01],
[3.7833e-01, -7.4251e-01],
[2.5751e-01, -7.9255e-01],
[1.3036e-01, -8.2307e-01],
[1.0205e-16, -8.3333e-01],
[-1.3036e-01, -8.2307e-01],
[-2.5751e-01, -7.9255e-01],
[-3.7833e-01, -7.4251e-01],
[-4.8982e-01, -6.7418e-01],
[-5.8926e-01, -5.8926e-01],
[-6.7418e-01, -4.8982e-01],
[-7.4251e-01, -3.7833e-01],
[-7.9255e-01, -2.5751e-01],
[-8.2307e-01, -1.3036e-01],
[-8.3333e-01, -1.5308e-16],
[-8.2307e-01, 1.3036e-01],
[-7.9255e-01, 2.5751e-01],
[-7.4251e-01, 3.7833e-01],
[-6.7418e-01, 4.8982e-01],
[-5.8926e-01, 5.8926e-01],
[-4.8982e-01, 6.7418e-01],
[-3.7833e-01, 7.4251e-01],
[-2.5751e-01, 7.9255e-01],
[-1.3036e-01, 8.2307e-01],
[0.0000e+00, 9.1667e-01],
[1.3046e-01, 9.0734e-01],
[2.5825e-01, 8.7954e-01],
[3.8080e-01, 8.3383e-01],
[4.9559e-01, 7.7115e-01],
[6.0029e-01, 6.9277e-01],
[6.9277e-01, 6.0029e-01],
[7.7115e-01, 4.9559e-01],
[8.3383e-01, 3.8080e-01],
[8.7954e-01, 2.5825e-01],
[9.0734e-01, 1.3046e-01],
[9.1667e-01, 2.5967e-16],
[9.0734e-01, -1.3046e-01],
[8.7954e-01, -2.5825e-01],
[8.3383e-01, -3.8080e-01],
[7.7115e-01, -4.9559e-01],
[6.9277e-01, -6.0029e-01],
[6.0029e-01, -6.9277e-01],
[4.9559e-01, -7.7115e-01],
[3.8080e-01, -8.3383e-01],
[2.5825e-01, -8.7954e-01],
[1.3046e-01, -9.0734e-01],
[5.1934e-16, -9.1667e-01],
[-1.3046e-01, -9.0734e-01],
[-2.5825e-01, -8.7954e-01],
[-3.8080e-01, -8.3383e-01],
[-4.9559e-01, -7.7115e-01],
[-6.0029e-01, -6.9277e-01],
[-6.9277e-01, -6.0029e-01],
[-7.7115e-01, -4.9559e-01],
[-8.3383e-01, -3.8080e-01],
[-8.7954e-01, -2.5825e-01],
[-9.0734e-01, -1.3046e-01],
[-9.1667e-01, -1.6839e-16],
[-9.0734e-01, 1.3046e-01],
[-8.7954e-01, 2.5825e-01],
[-8.3383e-01, 3.8080e-01],
[-7.7115e-01, 4.9559e-01],
[-6.9277e-01, 6.0029e-01],
[-6.0029e-01, 6.9277e-01],
[-4.9559e-01, 7.7115e-01],
[-3.8080e-01, 8.3383e-01],
[-2.5825e-01, 8.7954e-01],
[-1.3046e-01, 9.0734e-01],
[0.0000e+00, 1.0000e+00],
[1.3053e-01, 9.9144e-01],
[2.5882e-01, 9.6593e-01],
[3.8268e-01, 9.2388e-01],
[5.0000e-01, 8.6603e-01],
[6.0876e-01, 7.9335e-01],
[7.0711e-01, 7.0711e-01],
[7.9335e-01, 6.0876e-01],
[8.6603e-01, 5.0000e-01],
[9.2388e-01, 3.8268e-01],
[9.6593e-01, 2.5882e-01],
[9.9144e-01, 1.3053e-01],
[1.0000e+00, 6.1232e-17],
[9.9144e-01, -1.3053e-01],
[9.6593e-01, -2.5882e-01],
[9.2388e-01, -3.8268e-01],
[8.6603e-01, -5.0000e-01],
[7.9335e-01, -6.0876e-01],
[7.0711e-01, -7.0711e-01],
[6.0876e-01, -7.9335e-01],
[5.0000e-01, -8.6603e-01],
[3.8268e-01, -9.2388e-01],
[2.5882e-01, -9.6593e-01],
[1.3053e-01, -9.9144e-01],
[1.2246e-16, -1.0000e+00],
[-1.3053e-01, -9.9144e-01],
[-2.5882e-01, -9.6593e-01],
[-3.8268e-01, -9.2388e-01],
[-5.0000e-01, -8.6603e-01],
[-6.0876e-01, -7.9335e-01],
[-7.0711e-01, -7.0711e-01],
[-7.9335e-01, -6.0876e-01],
[-8.6603e-01, -5.0000e-01],
[-9.2388e-01, -3.8268e-01],
[-9.6593e-01, -2.5882e-01],
[-9.9144e-01, -1.3053e-01],
[-1.0000e+00, -1.8370e-16],
[-9.9144e-01, 1.3053e-01],
[-9.6593e-01, 2.5882e-01],
[-9.2388e-01, 3.8268e-01],
[-8.6603e-01, 5.0000e-01],
[-7.9335e-01, 6.0876e-01],
[-7.0711e-01, 7.0711e-01],
[-6.0876e-01, 7.9335e-01],
[-5.0000e-01, 8.6603e-01],
[-3.8268e-01, 9.2388e-01],
[-2.5882e-01, 9.6593e-01],
[-1.3053e-01, 9.9144e-01]])
# 三角剖分后每个有限元的中心点坐标
self.tripoints = np.loadtxt(
r'D:\Proj\EIT\EIT-py\data\tripoints_mean_py.csv', delimiter=",")
# 边界值柱状图ax1的横轴标签
self.index_ls = [
'AB', 'AC', 'AD', 'AE', 'AF', 'AG', 'AH', 'BC', 'BD', 'BE', 'BF',
'BG', 'BH', 'CD', 'CE', 'CF', 'CG', 'CH', 'DE', 'DF', 'DG', 'DH',
'EF', 'EG', 'EH', 'FG', 'FH', 'GH'
]
# 反投影图ax3的标注
self.notate = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
# 8个电极的圆心角
self.theta = [
2 * np.pi / 4, 1 * np.pi / 4, 0 * np.pi / 4, 7 * np.pi / 4,
6 * np.pi / 4, 5 * np.pi / 4, 4 * np.pi / 4, 3 * np.pi / 4
]
# 生成三角剖分对象
self.triang = Triangulation(self.nodes[:, 0], self.nodes[:, 1])
self.trifinder = self.triang.get_trifinder()
# 图像显示色谱
self.cmap = plt.cm.RdBu_r
self.norm = matplotlib.colors.Normalize(vmin=-1, vmax=1)
# ax1,ax2,ax3初始化
self.ax1_setup()
self.ax2_setup()
self.ax3_setup()
def ax1_setup(self):
"""ax1:边界数据柱状图"""
self.ax1 = self.fig.add_subplot(self.gs[0, :], aspect='auto')
plt.xticks(range(28), self.index_ls) #设置横坐标标签
plt.yticks([-10, 0, 10], c='none') #设置纵坐标刻度范围
plt.gca().xaxis.set_ticks_position('bottom')
plt.gca().spines['bottom'].set_position(('data', 0))
plt.gca().spines['right'].set_color('none')
plt.gca().spines['left'].set_color('none')
plt.gca().spines['top'].set_color('none')
def ax2_setup(self):
"""ax2:图像重建显示"""
self.ax2 = self.fig.add_subplot(self.gs[1:3, 0], aspect=1)
self.ax2.triplot(self.triang, linewidth=0.6, color='black') #绘制三角剖分网格
self.ax2.set_xticks([-1, 1])
self.ax2.set_yticks([-1, 1])
plt.axis('off')
self.cb = self.fig.colorbar(matplotlib.cm.ScalarMappable(
norm=self.norm, cmap="RdBu_r"),
ax=self.ax2) # 绘制色度条
self.cb.set_ticks([-1, 1])
def ax3_setup(self):
"""ax3:反投影路径"""
self.ax3 = self.fig.add_subplot(self.gs[1:3, 1],
projection='polar',
aspect=1)
for i in np.arange(8):
plt.annotate(self.notate[i],
xy=(self.theta[i], 1),
xytext=(self.theta[i], 1.1),
xycoords='data')
plt.axis('off')
def fe(self, elem_data):
"""以有限元填充方式显示重构图像"""
self.ax2.tripcolor(self.triang,
elem_data,
cmap="RdBu_r",
norm=self.norm,
shading='flat')
self.ax2.set_xticks([-1, 1])
self.ax2.set_yticks([-1, 1])
self.ax2.set_xticks([])
self.ax2.set_yticks([])
def interp(self, elem_data):
"""以矩阵插值方式显示重构图像"""
self.ax2.tripcolor(self.tripoints[:, 0],
self.tripoints[:, 1],
elem_data,
cmap="RdBu_r",
norm=self.norm,
shading='gouraud')
self.ax2.set_xticks([-1, 1])
self.ax2.set_yticks([-1, 1])
self.ax2.set_xticks([])
self.ax2.set_yticks([])
def contour(self, elem_data):
"""以轮廓图填充方式显示重构图像"""
self.ax2.tricontour(self.tripoints[:, 0],
self.tripoints[:, 1],
elem_data,
levels=5,
linewidths=0.5,
colors='k')
self.ax2.tricontourf(self.tripoints[:, 0],
self.tripoints[:, 1],
elem_data,
cmap="RdBu_r",
norm=self.norm,
levels=5)
self.ax2.set_xticks([-1, 1])
self.ax2.set_yticks([-1, 1])
self.ax2.set_xticks([])
self.ax2.set_yticks([])
plt.title('In triangle %i' % tri)
event.canvas.draw()
# Create a Triangulation.
n_angles = 16
n_radii = 5
min_radius = 0.25
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += np.pi / n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
triang = Triangulation(x, y)
triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
y[triang.triangles].mean(axis=1))
< min_radius)
# Use the triangulation's default TriFinder object.
trifinder = triang.get_trifinder()
# Setup plot and callbacks.
plt.subplot(aspect='equal')
plt.triplot(triang, 'bo-')
polygon = Polygon([[0, 0], [0, 0]], facecolor='y') # dummy data for (xs, ys)
update_polygon(-1)
plt.gca().add_patch(polygon)
plt.gcf().canvas.mpl_connect('motion_notify_event', on_mouse_move)
plt.show()
